Search results

This study aims to explore the associations linking employees’ challenge-hindrance appraisals toward artificial intelligence (AI) to service performance while considering the dual mediating roles of job crafting and job insecurity, as well as the moderating role of AI knowledge.

A survey was administered to a sample of 297 service industry employees. This study examined all the hypotheses with Mplus 8.0.

This study confirms that challenge appraisal toward AI has an indirect positive influence on service performance via job crafting (motivation process), whereas hindrance appraisal toward AI has an indirect negative influence on service performance via job insecurity (strain process). Meanwhile, AI knowledge, serving as a key personal resource, could strengthen the positive impacts of challenge appraisal toward AI on job crafting and of hindrance appraisal toward AI on job insecurity.

Organizational decision-makers should first survey employees’ appraisals toward AI and then adopt targeted managerial strategies. From the perspective of service industry employees, employees should adopt proactive coping strategies and enrich their knowledge of AI to meet the challenges brought by this technology.

The primary contribution of this study is that we enrich the literature on AI by exploring the dual mediators (i.e. job crafting and job insecurity) through which AI awareness affects service performance. Moreover, this study advances our understanding of when appraisals toward AI influence job outcomes by identifying the moderating role of AI knowledge.

Abnormal vibrations often occur in the liquid oxygen kerosene transmission pipelines of rocket engines, which seriously threaten their safety. Improper handling can result in failed rocket launches and significant economic losses. Therefore, this paper aims to examine vibrations in transmission pipelines.

In this study, a three-dimensional high-pressure pipeline model composed of corrugated pipes, multi-section bent pipes, and other auxiliary structures was established. The fluid–solid coupling method was used to analyse vibration characteristics of the pipeline under various external excitations. The simulation results were visualised using MATLAB, and their validity was verified via a thermal test.

In this study, the vibration mechanism of a complex high-pressure pipeline was examined via a visualisation method. The results showed that the low-frequency vibration of the pipe was caused by fluid self-excited pressure pulsation, whereas the vibration of the engine system caused a high-frequency vibration of the pipeline. The excitation of external pressure pulses did not significantly affect the vibrations of the pipelines. The visualisation results indicated that the severe vibration position of the pipeline thermal test is mainly concentrated between the inlet and outlet and between the two bellows.

The results of this study aid in understanding the causes of abnormal vibrations in rocket engine pipelines.

The causes of different vibration frequencies in the complex pipelines of rocket engines and the propagation characteristics of external vibration excitation were obtained.

The purpose of this study is to examine the impact of MoS₂ on the microstructure and characteristics of micro-arc oxidized (MAO) ceramic coatings created on ZK60 magnesium alloy through the addition of varying concentrations of MoS₂ particles to the electrolyte, aiming to enhance the corrosion resistance of magnesium alloy.

The surface morphology, roughness and phase composition of the coatings were analyzed using scanning electron microscopy, a hand-held roughness tester and an X-ray diffractometer, respectively, and the corrosion resistance of the MAO coatings prepared by the addition of different contents of MoS₂ particles was tested and analyzed using an electrochemical workstation.

The results demonstrate that MoS₂/MgO composite coatings have been successfully prepared on the surface of magnesium alloys through micro-arc oxidation. Furthermore, the corrosion resistance of the ZK60 magnesium alloy prepared with the addition of 1.0 g/L MoS₂ was the best compared to the other samples.

MoS₂ particles were able to penetrate the coatings successfully during the micro-arc oxidation process, acting as a barrier in the micropores to prevent the corrosion medium from touching the surface, thus improving the corrosion resistance of the sample. The electrochemical workstation was used to study the corrosion resistance of the MoS₂/MAO coating on the ZK60 magnesium alloy.

This study aims to investigate the effects of ball–material ratio on the properties of mixed powders and Cu-Bi self-lubricating alloy materials.

Cu-Bi mixed powder was ball milled at different ball–material ratios, and the preparation of Cu-Bi alloy materials was achieved through powder metallurgy technology. Scanning electron microscopy, X-ray diffraction and Raman spectroscopy were conducted to study the microstructure and phase composition of the mixed powder. The apparent density and flow characteristics of mixed powders were investigated using a Hall flowmeter. Tests on the crushing strength, impact toughness and tribological properties of self-lubricating alloy materials were conducted using a universal electronic testing machine, 300 J pendulum impact testing machine and M200 ring-block tribometer, respectively.

With the increase in ball–material ratio, the spherical copper matrix particles in the mixed powder became lamellar, the mechanical properties of the material gradually reduced, the friction coefficient of the material first decreased and then stabilized and the wear rate decreased initially and then increased. The increase in the ball–material ratio resulted in the fine network distribution of the Bi phase in the copper alloy matrix, which benefitted its enrichment on the worn surface for the formation a lubricating film and improvement of the material’s tribological performance. However, a large ball–material ratio can excessively weaken the mechanical properties of the material and reduce its wear resistance.

The effects of ball–material ratio on Cu-Bi mixed powder and material properties were clarified. This work provides a reference for the mechanical alloying process and its engineering applications.

This paper presents the experimental and numerical results of the bending properties of low-height prestressed T-beams. The purpose is to study the bearing capacity, failure state and strain distribution of low-height prestressed T-beams.

First, two 13 m-long full-size test beams were fabricated with different positions of prestressed steel bundles in the span. The load–deflection curves and failure patterns of each test beam were obtained through static load tests. Secondly, the test data were used to validate the finite element model developed to simulate the flexural behavior of low-height prestressed T-beams. Finally, the influence of different parameters (the number of prestressed steel bundles, initial prestress and concrete strength grade) on the flexural performance of the test beams is studied by using a finite element model.

The test results show that when the distance of the prestressed steel beam from the bottom height of the test beam increases from 40 to 120 mm, the cracking load of the test beam decreases from 550.00 to 450.00 kN, reducing by 18.18%, and the ultimate load decreases from 1338.15 to 1227.66 kN, reducing by 8.26%, therefore, the increase of the height of the prestressed steel beam reduces the bearing capacity of the test beam. The numerical simulation results show that when the number of steel bundles increases from 2 to 9, the cracking load increases by 183.60%, the yield load increases by 117.71% and the ultimate load increases by 132.95%. Therefore, the increase in the number of prestressed steel bundles can increase the cracking load, yield load and ultimate load of the test beam. When the initial prestress is from 695 to 1,395 MPa, the cracking load increases by 69.20%, the yield load of the bottom reinforcement increases by 31.61% and the ultimate load increases by 3.97%. Therefore, increasing the initial prestress can increase the cracking load and yield load of the test beam, but it has little effect on the ultimate load. The strength grade of concrete increases from C30 to C80, the cracking load is about 455.00 kN, the yield load is about 850.00 kN and the ultimate load is increased by 4.90%. Therefore, the improvement in concrete strength grade has little influence on the bearing capacity of the test beam.

Based on the experimental study, the bearing capacity of low-height prestressed T-beams with different prestressed steel beam heights is calculated by finite element simulation, and the influence of different parameters on the bearing capacity is discussed. This method not only ensures the accuracy of bearing capacity assessment, but also does not require a large number of samples and has a certain economy. The study of prestressed low-height T-beams is of great significance for understanding the principle and application of prestressed technology. Research on the mechanical behavior and performance of low-height prestressed T beams can provide a scientific basis and technical support for the design and construction of prestressed concrete structures. In addition, the study of prestressed low-height T-beams can also provide a reference for the optimization design and construction of other structural types.

Urbanization is driving the growth of China’s carbon footprint. It’s important to investigate what factors, how and to what extent, affect carbon footprints embedded in various categories of rural and urban households’ consumption.

We employ an environmental extended input-output model to assess and compare the rural-urban household carbon footprints and perform a multivariant regression analysis to identify the varying relationships of the determinants on rural and urban household carbon footprints based on the panel data of Chinese households from 2012 to 2018.

The results show evidence of urbanity density effect on direct carbon footprints and countervailing effect on indirect carbon footprints. The old dependency ratio has no significant effect on rural family emissions but has a significantly negative effect on urban direct and indirect carbon footprints. A higher child dependency ratio is associated with less rural household carbon emissions while the opposite is true for urban households. Taking advantage of recycled fuel saves direct carbon emissions and this green lifestyle benefits urban households more by saving more carbon emissions. There is a positive relationship between consumption structure ratio and direct carbon footprints while a negative relationship with indirect carbon footprints and this impact is less significant for urban households. The higher the price level of water, electricity and fuel, the lower the rural household’s direct carbon footprints. Private car ownership consistently augments household carbon footprints across rural and urban areas.

This paper provides comprehensive findings to understand the relationships between an array of determinants and China’s rural-urban carbon emissions, empowering China’s contribution to the global effort on climate mitigation.

This study aims to improve the offshore wind farm (OWF) site selection evaluation index system and establishes a decision-making model for OWF site selection. It is expected to provide helpful references for the progress of offshore wind power.

Firstly, this paper establishes an evaluation criteria system for OWF site selection, considering six criteria (wind resource, environment, economic, technical, social and risk) and related subcriteria. Then, the Criteria Importance Though Intercrieria Correlation (CRITIC) method is introduced to figure out the weights of evaluation indexes. In addition, the cumulative prospect theory and technique for order preference by similarity to an ideal solution (CPT-TOPSIS) method are employed to construct the OWF site selection decision-making model. Finally, taking the OWF site selection in China as an example, the effectiveness and robustness of the framework are verified by sensitivity analysis and comparative analysis.

This study establishes the OWF site selection evaluation system and constructs a decision-making model under the spherical fuzzy environment. A case of China is employed to verify the effectiveness and feasibility of the model.

In this paper, a new decision-making model is proposed for the first time, considering the ambiguity and uncertainty of information and the risk attitudes of decision-makers (DMs) in the decision-making process.

Warranty-based big data analysis has attracted a great deal of attention because of its key capabilities and role in improving product quality while minimizing costs. Information and details about particular parts (components) repair and replacement during the warranty term, usually stored in the after-sales service database, can be used to solve problems in a variety of sectors. Due to the small number of studies related to the complete analysis of parts failure patterns in the automotive industry in the literature, this paper focuses on discovering and assessing the impact of lesser-studied factors on the failure of auto parts in the warranty period from the after-sales data of an automotive manufacturer.

The interconnected method used in this study for analyzing failure patterns is formed by combining association rules (AR) mining and Bayesian networks (BNs).

This research utilized AR analysis to extract valuable information from warranty data, exploring the relationship between component failure, time and location. Additionally, BNs were employed to investigate other potential factors influencing component failure, which could not be identified using Association Rules alone. This approach provided a more comprehensive evaluation of the data and valuable insights for decision-making in relevant industries.

This study's findings are believed to be practical in achieving a better dissection and providing a comprehensive package that can be utilized to increase component quality and overcome cross-sectional solutions. The integration of these methods allowed for a wider exploration of potential factors influencing component failure, enhancing the validity and depth of the research findings.

The purpose of this paper is to explore the impact and mechanism of WeChat public platforms articles (abbreviated as WPP) on blood donation behavior using data of WPPA and donation behavior data.

This paper uses multiple linear regression methods, web crawlers and natural language processing technology. It first quantifies the impact of WPP published articles on donation behavior. On this basis, it then selects data from the day of article publication to further study the impact of article dissemination on donation behavior from the perspective of reading quantity, and analyzes the influencing factors of article reading quantity.

The results show that on the same day that an article is published, there is an increase of 13.8 and 14.3% in blood donation volume and fan registrations, respectively. The mediating effect exists. However, the day after an article is published, there is no longer any effect on blood donations. With a 1% increase in reading quantity, blood donation volume on the day of article publication increases by 0.13%, and this positive impact is promoted by the quality of the articles. A conc ise articles title and body and rich images help drive reading quantity. Moreover, blood donors prefer to read articles about blood dynamics and donation promotion, while articles about news, announcements and administrative affairs make them less inclined to read.

First, it focuses on WPPA, quantifies the impact of articles on blood donation behavior and analyzes the mechanism. Second, the authors study the impact and timeliness of social media article dissemination to address the insufficiency of existing research. Third, the study provides a scientific basis for the editing and publishing of articles, helping blood banks improve the effectiveness of publicity and recruitment.

For the provision of smart library services to end users, tools of the Internet of Things (IoT) play a significant role. The study aims to discover the factors influencing the adoption of IoT in university libraries, investigate the impact of IoT on university library services and identify challenges to adopt IoT applications in university libraries.

A systematic literature review was carried out to address the objectives of the study. The 40 most relevant research papers published in the world’s leading digital databases were selected to conduct the study.

The findings illustrated that rapid growth in technology, perceived benefits, the networked world and the changing landscape of librarianship positively influenced the adoption of IoT in university libraries. The study also displayed that IoT supported library professionals to initiate smart library services, assisted in service efficiency, offered context-based library services, provided tracking facilities and delivered effective management of library systems. Results also revealed that a lack of technical infrastructure, security and privacy concerns, a lack of technological skills and unavailability of policy and strategic planning caused barriers to the successful adoption of IoT applications in university libraries.

The study has provided theoretical implications through a valuable addition to the current literature. It has also offered managerial implications for policymakers to construct productive policies for the implementation of IoT applications in university libraries for the attainment of fruitful outcomes. Finally, the study provides a baseline for understanding the adoption of IoT in academic libraries.